Patent Application
20200088748
Metabolic profiling has significantly contributed to a deeper understanding of the biochemical metabolic networks and pathways in cells. A metabolite profile provides a snapshot of the complex interactions between genetic alterations, enzymatic activity, and biochemical reactions—revealing unique patterns of information that may be driven by specific genetic events. Metabolic profiling represents an extraordinary tool to profile cellular abnormalities and advance personalized medicine.
Aspects of the technology disclosed herein relate to methods of evaluating a biological sample, e.g., a formalin-fixed paraffin-embedded (FFPE) preparation of a biological sample. In some aspects, the method comprises obtaining an FFPE preparation of the biological sample and detecting the presence of one or more metabolites in the FFPE preparation, wherein the one or more metabolites are members of a class selected from the classes listed in Table 1. In some aspects, the method comprises obtaining an FFPE preparation of the biological sample and detecting the presence of one or more metabolites in the FFPE preparation, wherein the one or more metabolites are members of a subclass selected from the subclasses listed in Table 1. In some aspects, the method comprises obtaining an FFPE preparation of the biological sample and detecting the presence of one or more metabolites in the FFPE preparation, wherein the one or more metabolites comprise a substituent group selected from the substituents listed in Table 1.
In some embodiments, the one or more metabolites are lipids. In some embodiments, the one or more metabolites are unsaturated fatty acids. In some embodiments, the one or more metabolites are hydrophobic metabolites. In some embodiments, the one or more metabolites are selected from taurine, 1-palmitoylglycerophosphoinositol, pyroglutamine, oxidized glutathione, dihomo-linoleate, creatinine, 1-linoleoylglycerophosphoethanolamine, eicosenoate, and 10-nonadecenoate.
In some embodiments, the one or more metabolites do not include one or more metabolites that are members of a class listed in Table 2. In some embodiments, the one or more metabolites do not include one or more metabolites that are members of a subclass listed in Table 2. In some embodiments, the one or more metabolites are not peptides. In some embodiments, the one or more metabolites are not steroids.
In some embodiments, the presence of 2 or more metabolites are detected in the FFPE preparation. In some embodiments, the presence of 5 or more metabolites are detected in the FFPE preparation. In some embodiments, the presence of 10 or more metabolites are detected in the FFPE preparation. In some embodiments, the presence of 25 or more metabolites are detected in the FFPE preparation.
In some embodiments, methods provided herein further comprise measuring an expression level of the one or more metabolites in the FFPE preparation. In some embodiments, the methods further comprise comparing the expression level of the one or more metabolites measured in the FFPE preparation to an expression level of the one or more metabolites measured in a control sample. In some embodiments, the one or more metabolites are selected from the metabolites listed in Table 3. In some embodiments, the FFPE preparation and the control sample are biological samples of the same subject. In some embodiments, the FFPE preparation and the control sample are biological samples of different subjects.
In some embodiments, the control sample is a biological sample of non-cancerous tissue. In such embodiments, methods provided herein further comprise identifying the FFPE preparation as comprising cancerous tissue when the one or more metabolites are differentially expressed in the FFPE preparation when compared to the control sample.
In some embodiments, the control sample is a biological sample of cancerous tissue. In such embodiments, methods provided herein further comprise identifying the FFPE preparation as not comprising cancerous tissue when the one or more metabolites are differentially expressed in the FFPE preparation when compared to the control sample.
In some embodiments, the one or more differentially expressed metabolites are selected using a criteria of p-value <0.05. In some embodiments, the one or more differentially expressed metabolites are selected using a criteria of false discovery rate <0.1.
In some embodiments, methods provided herein further comprise determining tumor status of the biological sample based on the measuring of one or more metabolites in the FFPE preparation.
In some embodiments, the biological sample is a tissue sample. In some embodiments, the tissue sample is a prostate tissue sample.
In some embodiments, methods provided herein further comprise extracting the one or more metabolites from the FFPE biological sample. In some embodiments, the one or more metabolites are extracted using a methanol solution. In some embodiments, the methanol solution comprises 80% methanol.
In some embodiments, methods provided herein further comprise staining the FFPE biological sample for histological analysis. In some embodiments, the FFPE biological sample is stained using H&E stain.
In some embodiments, methods provided herein further comprise measuring the one or more metabolites in two or more portions of the FFPE preparation of the biological sample.
In some embodiments of any one of the methods described herein, the FFPE preparation is mounted on a slide. In some embodiments, FFPE preparation that is mounted on a slide is a section of tissue. In some embodiments, FFPE preparation that is mounted on a slide comprises cells (e.g., those cultured on a surface). In some embodiments, extracting one or more metabolites from an FFPE biological sample that is mounted on or attached to a slide when the slide is situated in a cassette. In some embodiments, a cassette reduces the volume of extraction solution so as to increase the yield of extracted metabolites in the solution. In some embodiments, a cassette has the design depicted in
In some embodiments of any one of the methods described herein, one or more metabolites are detected in an FFPE preparation and normalized (e.g., when comparing to another FFPE preparation) by weight of the sample assessed (e.g., per ng of tissue). In some embodiments, normalization is done using a housekeeping metabolite. In some embodiments, a housekeeping metabolite is cytidine 50-diphosphocholine. In some embodiments, normalization between a test sample (e.g., diseased tissue) and a control sample (e.g., non-diseased tissue) is done using a housekeeping metabolite (e.g., cytidine 50-diphosphocholine). In some embodiments, a housekeeping metabolite is a metabolite selected from Table 27. A house keeping metabolite is one whose expression does not change between the conditions that are being compared (e.g., diseased and non-diseased tissue).
In some embodiments of any one of the methods described herein, one or more metabolites are detected in an FFPE preparation and normalized (e.g., when comparing to another FFPE preparation) by the number of a particular tissue compartment (e.g., epithelial cells), cellular compartment (e.g., nucleus), or a particular area of tissue compartment (e.g., area of epithelium or stromal compartment). In some embodiments, metabolite expression data is normalized using one or more metabolites selected from Table 31, Table 32 or Table 33. In some embodiments, metabolite expression data is normalized using fructose, glycine, guanine, or phenylalanine. Metabolites that can be used to normalize metabolite expression data may be identified using a combination of metabolite expression analysis and image analysis, and optionally correlating the metabolite expression levels to the image analysis unit (e.g., number of cells, area of cells, or number of nuclei).
The skilled artisan will understand that the figures, described herein, are for illustration purposes only. It is to be understood that, in some instances, various aspects of the invention may be shown exaggerated or enlarged to facilitate an understanding of the invention. In the drawings, like reference characters generally refer to like features, functionally similar and/or structurally similar elements throughout the various figures. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the teachings. The drawings are not intended to limit the scope of the present teachings in any way.
The features and advantages of the present invention will become more apparent from the detailed description set forth below when taken in conjunction with the drawings.
When describing embodiments in reference to the drawings, direction references (“above,” “below,” “top,” “bottom,” “left,” “right,” “horizontal,” “vertical,” etc.) may be used. Such references are intended merely as an aid to the reader viewing the drawings in a normal orientation. These directional references are not intended to describe a preferred or only orientation of an embodied device. A device may be embodied in other orientations.
As is apparent from the detailed description, the examples depicted in the figures and further described for the purpose of illustration throughout the application describe non-limiting embodiments, and in some cases may simplify certain processes or omit features or steps for the purpose of clearer illustration.
Among other aspects, the present disclosure provides techniques capable of identifying metabolites in FFPE samples. The process of generating an FFPE preparation of a biological specimen generally requires the use of chemically reactive conditions, which can make obtaining reliable metabolic data from these preparations difficult. The methods provided in the disclosure relate, at least in part, to the recognition that certain metabolites are capable of being detected and/or measured in FFPE preparations of biological samples. As described herein, such methods were utilized to successfully measure levels of differentially expressed metabolites, e.g., to determine tumor status in the biological sample. Surprisingly, the mild conditions applied in the preparation and/or extraction techniques presented herein allow for secondary analyses to be conducted on the same FFPE preparation of the biological sample, permitting a comprehensive analysis of the metabolic state and tissue architecture in a single biological sample.
Metabolites are small molecule compounds, such as substrates for enzymes of metabolic pathways, intermediates of such pathways or the products obtained by a metabolic pathway. Metabolic pathways are well known in the art, and include, for example, citric acid cycle, respiratory chain, glycolysis, gluconeogenesis, hexose monophosphate pathway, oxidative pentose phosphate pathway, production and (3-oxidation of fatty acids, urea cycle, amino acid biosynthesis pathways, protein degradation pathways, amino acid degrading pathways, and biosynthesis or degradation of lipids, proteins, and nucleic acids. Accordingly, small molecule compound metabolites may be composed of, but are not limited to, the following classes of compounds: alcohols, alkanes, alkenes, alkynes, aromatic compounds, ketones, aldehydes, carboxylic acids, esters, amines, imines, amides, cyanides, amino acids, peptides, thiols, thioesters, phosphate esters, sulfate esters, thioethers, sulfoxides, ethers, or combinations or derivatives of the aforementioned compounds.
In some embodiments, a metabolite has a molecular weight of 50 Da (Dalton) to 30,000 Da, e.g., less than 30,000 Da, less than 20,000 Da, less than 15,000 Da, less than 10,000 Da, less than 8,000 Da, less than 7,000 Da, less than 6,000 Da, less than 5,000 Da, less than 4,000 Da, less than 3,000 Da, less than 2,000 Da, less than 1,000 Da, less than 500 Da, less than 300 Da, less than 200 Da, less than 100 Da. In some embodiments, a metabolite has a molecular weight of at least 50 Da. In some embodiments, a metabolite has a molecular weight of 50 Da up to 1,500 Da. In some embodiments, a metabolite contemplated in the techniques described herein is any metabolite isolated from or identified in a biological sample.
As used herein, in some embodiments, the term “biological sample” refers to a sample derived from a subject, e.g., a patient. Non-limiting examples of a biological sample include blood, serum, urine, and tissue. In some embodiments, the biological sample is tissue. Obtaining a biological sample of a subject means taking possession of a biological sample of the subject. Obtaining a biological sample from a subject, in some embodiments, means removing a biological sample from the subject. Therefore, the person obtaining a biological sample of a subject and measuring a profile of metabolites in the biological sample does not necessarily obtain the biological sample from the subject. In some embodiments, the biological sample may be removed from the subject by a medical practitioner (e.g., a doctor, nurse, or a clinical laboratory practitioner), and then provided to the person measuring a profile of metabolites. The biological sample may be provided to the person measuring a profile of metabolites by the subject or by a medical practitioner (e.g., a doctor, nurse, or a clinical laboratory practitioner). In some embodiments, the person measuring a profile of metabolites obtains a biological sample from the subject by removing the sample from the subject.
As used herein, a “subject” refers to any mammal, including humans and non-humans, such as primates. In some embodiments, the subject is a human, and has been diagnosed or is suspected of having a tumor. In some embodiments, the subject may be diagnosed or is suspected of having a prostate tumor.
It is to be understood that a biological sample may be processed in any appropriate manner to facilitate measuring expression levels of metabolic profiles. For example, in some embodiments, biochemical, mechanical and/or thermal processing methods may be appropriately used to isolate a biomolecule of interest from a biological sample. The expression levels of the metabolites may also be determined in a biological sample directly. The expression levels of the metabolites may be measured by performing an assay, such as but not limited to, mass spectroscopy, positron emission tomography, gas chromatography (GC-MS) or HPLC liquid chromatography (LC-MS). Other appropriate methods for determining levels of metabolites will be apparent to the skilled artisan.
In some aspects, techniques described herein may be used to detect the presence of one or more metabolites in a biological sample (e.g., an FFPE preparation of a biological sample). In some embodiments, the one or more metabolites may be classified according to conventional classification constructs, nomenclature known in the art, and/or structural features of the one or more metabolites. For example, in some embodiments, the one or more metabolites may include 10-nonadecenoate and 1-palmitoyl glycerophosphoinositol. In some embodiments, 10-nonadecenoate and 1-palmitoyl glycerophosphoinositol can both be classified as fatty acids (e.g., Class: “Fatty Acids”). In some embodiments, 10-nonadecenoate and 1-palmitoyl glycerophosphoinositol can be further subdivided into subclasses according to the structural properties of each molecule. In such embodiments, 10-nonadecenoate may be classified as an unsaturated fatty acid (e.g., Subclass: “Unsaturated Fatty Acids”) and 1-palmitoyl glycerophosphoinositol may be classified as a lysophosphatidylinositol (e.g., Subclass: “Lysophosphatidylinositols”). In some embodiments, 10-nonadecenoate and 1-palmitoyl glycerophosphoinositol can be further subdivided according to the substituents present in each molecule. In such embodiments 10-nonadecenoate may be classified according to its carboxylate substituent (e.g., Substituent: “Carboxylic Acid”) and 1-palmitoyl glycerophosphoinositol may be classified according to its ester substituent (e.g., Substituent: “Fatty Acid Ester”). Accordingly, in some embodiments, classifying the one or more metabolites may be used to assess the biological sample and/or the techniques used in detecting the one or more metabolites (e.g., methods of extraction, methods of measuring metabolites, etc.).
In some embodiments, the one or more metabolites are members of a class selected from the classes listed in Table 1. In some embodiments, the one or more metabolites are members of a subclass selected from the subclasses listed in Table 1. In some embodiments, the one or more metabolites comprise a substituent group selected from the substituents listed in Table 1.
In some embodiments, methods described herein relate to the detection of at least one metabolite that is capable of being classified according to at least one of the classes, at least one of the subclasses, and at least one of the substituents listed in Table 1. In some embodiments, methods described herein relate to the detection of a plurality of metabolites, each of which are capable of being classified according to at least one of the classes, subclasses, and substituents listed in Table 1. In some embodiments, the plurality of metabolites contemplated in the methods described herein include a set of metabolites that are representative of at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 25, at least 30 of the classes listed in Table 1. In some embodiments, the plurality of metabolites contemplated in the methods described herein include a set of metabolites that are representative of at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 25, at least 30, at least 40, at least 50 of the subclasses listed in Table 1. In some embodiments, the plurality of metabolites contemplated in the methods described herein include a set of metabolites that are representative of at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 25, at least 30, at least 40, at least 50 of the substituents listed in Table 1.
In some embodiments, the one or more metabolites do not include one or more metabolites that are members of a class selected from the classes listed in Table 2. In some embodiments, the one or more metabolites do not include one or more metabolites that are members of a subclass selected from the subclasses listed in Table 2. In some embodiments, the one or more metabolites do not comprise a substituent group selected from the substituents listed in Table 2.
In some aspects, techniques provided by the present disclosure may be performed in a comparative format. For example, in some embodiments, the one or more metabolites detected in the methods described herein are differentially expressed in a tumor sample versus a control sample. By “differentially expressed” it means that the average expression of a metabolite in a tumor sample has a statistically significant difference from that in a control sample. For example, a significant difference that indicates differentially expressed metabolites may be detected when the expression level of the metabolite in a tumor sample is at least 1%, at least 5%, at least 10%, at least 25%, at least 50%, at least 100%, at least 250%, at least 500%, or at least 1000% higher, or lower, than that of a control sample. Similarly, a significant difference may be detected when the expression level of a metabolite in a tumor sample is at least 2-fold, at least 3-fold, at least 4-fold, at least 5-fold, at least 6-fold, at least 7-fold, at least 8-fold, at least 9-fold, at least 10-fold, at least 20-fold, at least 30-fold, at least 40-fold, at least 50-fold, at least 100-fold, or more higher, or lower, than that of a control sample. Significant differences may be identified by using an appropriate statistical test. Tests for statistical significance are well known in the art and are exemplified in Applied Statistics for Engineers and Scientists by Petruccelli, Chen and Nandram 1999 Reprint Ed. In some embodiments, the differentially expressed metabolites are selected using a criteria of false discovery rate <0.2. In some embodiments, the differentially expressed metabolites are selected using a criteria of p-value <0.05. P-value looks at the average concentration of the metabolite in the two groups and reports the likelihood that the difference in the concentration between the two groups occurs by chance. As described in further detail in the Examples, a number of differentially expressed metabolites have already been identified using some of the methods provided herein. These metabolites, which were differentially expressed in tumor tissue (e.g., prostate cancer) versus control tissue with a p-value <0.05, are reported in Table 3.
In some embodiments, a control sample may be used in a comparative analysis in evaluating an FFPE preparation of a biological sample (e.g., a tumor sample). In some embodiments, a sample of interest (e.g., a tumor sample) and a control sample are biological samples of the same subject. In some embodiments, the sample of interest and the control sample are biological samples of different subjects. In some embodiments, the control sample is a biological sample of non-cancerous tissue. In some embodiments, the control sample is a biological sample of cancerous tissue. In some embodiments, the sample of interest is a biological sample having or suspected of having tumorous tissue. In some embodiments, the sample of interest is a prostate tissue sample. In some embodiments, the control sample is a prostate tissue sample.
In some embodiments, the one or more metabolites detected in the methods described herein are selected from Table 3. In some embodiments, any subset of at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50 of the metabolites of Table 3 are detected in the methods described herein. Examples of a subset of metabolites used in the methods described herein include, but are not limited to, the first 5, 10, 15, 20, 25, 30, 35, 40, 45, or 50 metabolites or the last 5, 10, 15, 20, 25, 30, 35, 40, 45, or 50 metabolites or any combination of 5, 10, 15, 20, 25, 30, 35, 40, 45, or 50 metabolites of Table 3. A non-limiting example of a subset of at least 10 metabolites used in the methods described herein is Taurine, 1-palmitoylglycerophosphoinositol, pyroglutamine, glutathione, oxidized, dihomo-linoleate, creatinine, 1-linoleoylglycerophosphoethanolamine, eicosenoate, 10-nonadecenoate, and 1-oleoylglycerophosphoinositol.
FFPE cell or tissue samples may be prepared according to protocols commonly used in the art (e.g., see Canene-Adams, K. Methods Enzymol. 2013; 533:225-33; and Hewitt, S. M., et al. Arch Pathol Lab Med. 2008; 132:1929-35). Typically, sections of paraffin-embedded cells or tissues are obtained by: (a) preserving a tissue in fixative, (b) dehydrating the fixed tissue, (c) infiltrating the tissue with fixative, (d) orienting the tissue such that the cut surface accurately represents the tissue, (e) embedding the tissue in paraffin (e.g., making a paraffin block), and (f) cutting tissue paraffin block with microtome into sections. In some embodiments, an FFPE preparation of a biological sample is analyzed by punching a core from the tissue paraffin block.
In some embodiments, methods described herein relate to the evaluation of an FFPE preparation of a biological sample. In some embodiments, multiple portions of a single FFPE preparation can be evaluated. For example, in some embodiments, two or more portions (e.g., punches, slices, etc.) of an FFPE preparation of a biological sample are obtained, and each portion is subjected to a separate analysis (e.g., evaluating the presence or absence of one or more metabolites). Such an approach can advantageously allow the practitioner to delineate normal tissue (e.g., healthy) and abnormal tissue (e.g., tumorous) within the three-dimensional architecture of the FFPE preparation. In some embodiments, the FFPE preparation is subjected to a metabolite extraction.
Metabolite extractions may be conducted according to any suitable methods known in the art. For example, in some embodiments, the conditions of an extraction method may be dependent upon the chemical and/or physical properties of the molecules (e.g., metabolites) that are targeted for a particular analysis. For example, in some embodiments, it may be desirable to extract polar metabolites. In such instances, a methanol solution may be used to extract polar metabolites in an FFPE preparation. Alternatively, in some embodiments, it may be desirable to favor extraction of non-polar metabolites. In such instances, a chloroform solution may be used to extract non-polar metabolites.
In some embodiments, methods described herein involve a metabolite extraction step. In some embodiments, metabolites are extracted from an FFPE preparation using a methanol solution (e.g., methanol in water). In some embodiments, the methanol solution is approximately 80% methanol. In some embodiments, the methanol solution is between about 50% methanol and about 60% methanol, between about 60% methanol and about 65% methanol, between about 65% methanol and about 70% methanol, between about 70% methanol and about 75% methanol, between about 75% methanol and about 80% methanol, between about 80% methanol and about 85% methanol, between about 85% methanol and about 90% methanol, between about 90% methanol and about 95% methanol, or between about 95% methanol and about 99% methanol. The methods disclosed herein typically comprise determining the presence of one or more metabolites in an FFPE preparation of a biological sample. In some embodiments, at least 5, at least 10, at least 25, at least 50, at least 75, at least 100, at least 125, at least 150, at least 175, at least 200, at least 225, at least 250, at least 500, at least 750, at least 1000 or at least 1500 metabolites are measured. In some embodiments, provided methods include measuring a level of expression of differentially expressed metabolites in a tumor sample versus a control sample. In some embodiments, at least 5, at least 10, at least 25, at least 50, at least 75, at least 100, at least 125, at least 150, at least 175, at least 200, at least 225, at least 250, at least 500, at least 750, at least 1000 or at least 1500 differentially expressed metabolites are measured.
In some embodiments, techniques described herein may be used to evaluate tumor status of a biological sample. As used herein, “tumor status” refers to the biological state of a sample with respect to any tumorous tissue. For example, in some embodiments, the tumor status of a tissue refers to the overall presence or absence of a tumor in the tissue sample. In some embodiments, methods of the disclosure may be used to provide additional information related to the tumor status of a tissue sample, such as whether the sample has benign, pre-malignant, or malignant tumorous tissue. In some embodiments, methods of the disclosure can further indicate the severity of a tumor in a tissue sample (e.g., indolent versus aggressive cancer). In some embodiments, tumor status is assessed based on a comparative analysis that involves evaluating differential expression of metabolites in tumor versus control samples.
Methods of the disclosure relate, in some embodiments, to the evaluation of an FFPE biological sample. As described herein, such samples may be evaluated using minimally invasive methods, e.g., chemical extraction of metabolites. In some embodiments, these techniques preserve the architectural landscape of the FFPE sample such that it may be subjected to additional evaluative procedures. For example, in some embodiments, the FFPE biological sample is subjected to metabolite extraction and subsequently stained for histological analysis (e.g., using any suitable histological stain such as alcian blue, Fuchsin, haematoxylin and eosin (H&E), Masson trichrome, toluidine blue, Wright's/Giemsa stain, and combinations thereof). Accordingly, in some embodiments, the methods described herein provide a comprehensive analysis at both the biochemical level and cellular level.
A report summarizing the results of the analysis, e.g., tumor status of the sample and any other information pertaining to the analysis could optionally be generated as part of the analysis (which may be interchangeably referred to herein as “providing” a report, “producing” a report, or “generating” a report). Examples of reports may include, but are not limited to, reports in paper (such as computer-generated printouts of test results) or equivalent formats and reports stored on computer readable medium (such as a CD, computer hard drive, or computer network server, etc.). Reports, particularly those stored on computer readable medium, can be part of a database (such as a database of patient records, which may be a “secure database” that has security features that limit access to the report, such as to allow only the patient and the patient's medical practitioners to view the report, for example). In addition to, or as an alternative to, generating a tangible report, reports can also be displayed on a computer screen (or the display of another electronic device or instrument).
A report can further be transmitted, communicated or reported (these terms may be used herein interchangeably), such as to the individual who was tested, a medical practitioner (e.g., a doctor, nurse, clinical laboratory practitioner, genetic counselor, etc.), a healthcare organization, a clinical laboratory, and/or any other party intended to view or possess the report. The act of ‘transmitting’ or ‘communicating’ a report can be by any means known in the art, based on the form of the report, and includes both oral and non-oral transmission. Furthermore, “transmitting” or “communicating” a report can include delivering a report (“pushing”) and/or retrieving (“pulling”) a report. For example, non-oral reports can be transmitted/communicated by such means as being physically transferred between parties (such as for reports in paper format), such as by being physically delivered from one party to another, or by being transmitted electronically or in signal form (e.g., via e-mail or over the internet, by facsimile, and/or by any wired or wireless communication methods known in the art), such as by being retrieved from a database stored on a computer network server, etc.
In some aspects, methods provided in the present disclosure may be conducted in an apparatus (e.g., a cassette) designed to accommodate a tissue section attached to a slide, as shown in the schematic embodiment depicted in
It should be understood that the cassettes described above may have any appropriate combination of dimensions and/or volumes. For example, in one embodiment, the various structures of the cassette and may be constructed and arranged such that the cassette uses a relatively small volume of solvent for extraction of the metabolite. In such an embodiment, the volume of a portion of a chamber between a sample side of a slide or one or more restraints and an opposing side of the chamber may be between or equal to 0.5 and 3 mL, 1 and 2 mL, 1.5 and 5 mL, 2 and 10 mL and/or any other appropriate volume.
In one embodiment, a cassette may have an overall length between an opening and opposing bottom chamber surface of the chamber of about 75 mm. The distance between the one or more restraints and the bottom surface of the chamber may be about 50 mm. The distance between the one or more restraints and a side of the chamber a slide may be disposed against may be about 1.5 mm. A distance between the one or more restraints and a side of the chamber opposite the slide defining a volume the sample is exposed to may be between about 1.5 and 5 mm, 1.5 mm and 4 mm, 2 m, and 3 mm, and/or any other appropriate distance. The above described ramp may extend over a width of the chamber of about 5 mm and about 25 mm inwards from the opening into an interior of the chamber towards the opposing bottom surface of the chamber. While particular dimensions are noted above, it should be understood that any appropriate combination and/or ranges of dimensions may be used including dimensions both greater and small than those dimensions noted above as the disclosure is not so limited.
The present invention is further illustrated by the following Examples, which in no way should be construed as further limiting. The entire contents of all of the references (including literature references, issued patents, published patent applications, and co pending patent applications) cited throughout this application are hereby expressly incorporated by reference.
LNCaP prostate cells were grown in RPMI-1640 media supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. LNCaP-Ab1 (passage #81) cells were grown in RPMI-1640 media supplemented with 10% FBS Charcoal Dextran Stripped and 1% penicillin-streptomycin at 37° C. and 5% CO2. Both cell lines were authenticated and tested mycoplasma free. About 5×10−6 cells were plated in a 10-cm dish. Prior to sample preparation (48 hrs after seeding), cells on the dish were washed three times with phosphate buffer solution (PBS).
To prepare frozen samples, adherent cells were directly quenched with 1 mL of 80% methanol in the dish culture to avoid trypsin use, and cells were gently detached using a cell lifter. The methanol solution containing the quenched cells was pipetted into a 2 mL centrifuge tube for extraction. In the case of FFPE samples, the adherent cells were directly quenched with 1 mL of 4% formalin. The formalin solution was kept in the culture dish for 20 minutes at room temperature. Then, the adherent cells were washed three times with PBS, detached using a cell lifter, and then embedded in paraffin following the standard procedure.
The detailed protocol to produce flash-frozen cell line samples is the following: 1) Change the medium of the cell dishes 2 hours before metabolite extraction; 2) Aspirate the medium completely; 3) Wash the dishes 2-3 times with PBS; 4) Put the dishes on dry ice and add 1 mL of 80% methanol (cooled to −80° C.); 5) Incubate the dishes at −80° C. for 20 minutes; 6) Scrape the dishes on dry ice with cell scraper; 7) Transfer the cell lysate/methanol mixture to a 15 mL conical tube on dry ice; 8) Centrifuge the tube at 14,000 g for 5 minutes to pellet the cell debris; 9) Transfer the metabolite-containing supernatant to a new tube; 10) Dry the metabolite-containing supernatant using no heat; 11) The dried metabolite samples can be stored at −80° C. for several weeks.
The detailed protocol to produce FFPE cell line samples is the following: 1) Change the medium of the cell dishes 2 hours before metabolite extraction; 2) Aspirate the medium completely; 3) Wash the dishes 2-3 times with PBS; 4) Add 1 mL of 4% formalin to each dish; 5) Incubate the dishes at room temperature for 20 minutes; 6) Aspirate the 4% formalin solution completely; 7) Wash the dishes 2-3 times with PBS; 8) Scrape the dishes with cell scraper; 9) Transfer the fixated cells into a cassette; 10) Embed the fixated cells in paraffin using the standard procedure; 11) Place FFPE cells in a 1.5 mL micro-centrifuge tube; 12) Prepare the FFPE extracts following the protocol to extract the metabolites from FFPE material; 13) The dried metabolite samples can be stored at −80° C. for several weeks.
Samples from radical prostatectomies were utilized in the study. Both Optimal Cutting Temperature (OCT)-embedded and FFPE tissue blocks were collected from each prostatectomy. Tissue blocks were sectioned at 5 μm and were stained with H&E to identify tumor and normal area in each block. Sections of 20 μm were stained with H&E to evaluate the tissue architecture. Histopathology evaluation was performed to assess the percentage of tumor and the Gleason score in each of the tissue samples. From each tissue block were collected 2-mm biopsy punch samples from both the tumor and normal tissue compartment.
Slide-mounted tissue sections, regions enriched for normal and tumor epithelial cells were dissected manually. An Area Of Interest (AOI) was hand annotated by a pathologist (M.L.) on an H&E stained, cover slipped, slide-mounted tissue section. This section was then manually aligned and traced onto the back of a second non-cover slipped slide containing a serially cut tissue section from the same tissue block. Manual macro-dissection was then performed on the second slide using a scalpel or razor blade to remove the tissue out of AOI. H&E slides were scanned using Vectra Intelligent Slide Analysis System 2.0.8.
H&E slides were scanned using Vectra Intelligent Slide Analysis System 2.0.8 (Perkin Elmer) using the tissue scanning protocol at optimal setting. Bright-field images acquired at 4× and 20× were then used to develop semi-automated image analysis algorithms using inform Advanced Image Analysis Software 2.0.5 (Perkin Elmer). Full slide batches of images were processed automatically and edited for quality. Images acquired at 4× (full-resolution RGB) with resolution factored two times higher were used in trainable tissue segmentation. Developed algorithm was confident in distinguishing epithelium and stroma, but not tumor and benign tissue. Each image was reviewed by a pathologist and manually edited to distinguish tumor and benign tissue. An algorithm was developed on 20× images (full-resolution RGB) converted to optical density for trainable cell segmentation. Pre-set spectral libraries of Hematoxylin (blue hematox) and eosin from Nuance 3.0 (Perkin Elmer) were applied against a blank slide as white background. Nuclear segmentation based on blue hematox component, minimum signal 0.30 (on a value scale ranging from 0 to 1), minimum size 40 pixels and maximum size 400 pixels, and refined splitting with minimum circularity of 0.2. Total cells were counted in epithelium and stroma with the percentage area of the nuclei.
Metabolite Extraction with Methanol
The metabolome from frozen samples was extracted incubating the tissue in 1 mL of 80% methanol at room temperature on a benchtop for 4 hrs. After centrifugation at 14,000 g (10 minutes), the supernatant was collected and stored at −80° C. Metabolite extraction from FFPE samples was performed by adding 1 mL of 80% methanol directly to the sample and incubating at 70° C. for 30-45 minutes in a 1.5-mL micro-centrifuge tube without any de-paraffinization procedure (12). The sample was then placed on ice for 15 minutes and centrifuged at 14,000 g for 10 minutes (4-8° C.). The supernatant was transferred into a new 1.5-mL micro-centrifuge tube and chilled on ice for 10 minutes, followed by centrifugation at 14,000 g for 5 minutes (4-8° C.). Finally, the supernatant was collected and stored at −80° C. A schematic overview of the procedure is shown in
Metabolite profiling was conducted as previously described and further detailed in the below (13).
Sample Preparation:
The sample preparation process was carried out using the automated MicroLab STAR® system. Recovery standards were added prior to the first step in the extraction process for Quality Control (QC) purposes. Sample preparation was conducted using a series of organic and aqueous extractions to remove the protein fraction while allowing maximum recovery for small molecules. The resulting extract was divided into two fractions; one for analysis by LC and one for analysis by GC. Samples were placed briefly on a TurboVap® to remove the organic solvent. Each sample was then frozen and dried under vacuum. Samples were then prepared for either LC-MS or GC-MS, accordingly.
For Quality Assurance (QA)/QC purposes, a number of additional samples were included with each day's analysis. Furthermore, a selection of QC compounds was added to every sample, including those under test. These compounds were carefully chosen so as not to interfere with the measurement of the endogenous compounds. These QC samples were primarily used to evaluate the process control for each study as well as aiding in the data curation.
Ultrahigh performance liquid chromatography/Mass Spectroscopy (UPLC-MS/MS):
The LC-MS portion of the platform was based on a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo-Finnigan linear trap quadrupole (LTQ) mass spectrometer, which consists of an electrospray ionization (ESI) source and linear ion-trap (LIT) mass analyzer. The sample extract was dried and then reconstituted in acidic or basic LC-compatible solvents, each of which contained 8 or more injection standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot was analyzed using acidic positive ion optimized conditions and the other using basic negative ion optimized conditions in two independent injections using separate dedicated columns. Extracts reconstituted in acidic conditions were gradient eluted using water and methanol containing 0.1% formic acid, while the basic extracts, which also used water/methanol, contained 6.5 mM Ammonium Bicarbonate. The MS analysis alternated between MS and data-dependent MS2 scans using dynamic exclusion.
Gas chromatography/Mass Spectroscopy (GC-MS):
The samples destined for GC-MS analysis were re-dried under vacuum desiccation for a minimum of 24 hrs, prior to being derivatized under dried nitrogen using bistrimethyl-silyl-triflouroacetamide (BSTFA). The GC column was 5% phenyl and the temperature ramp was from 40° C. to 300° C. in a 16 minute period. Samples were analyzed on a Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole mass spectrometer using electron impact ionization. The instrument was tuned and calibrated for mass resolution and mass accuracy on a daily basis.
Accurate mass determination and MS/MS fragmentation (LC-MS), (LC-MS/MS):
The LC-MS portion of the platform was based on a Water ACQUITY UPLC and a Thermo-Finnigan LTQ-FT mass spectrometer, which had a linear ion-trap (LIT) front-end and a Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer backend. For ions with counts greater than 2 million, an accurate mass measurement could be performed. Accurate mass measurements could be made on the parent ion as well fragments. The typical mass error was less than 5 ppm.
Data quality: instrument and process variability:
Instrument variability was determined by calculating the median relative standard deviation (RSD) for the internal standards that were added to each sample prior to injection into the mass spectrometers. Overall process variability was determined by calculating the median RSD for all endogenous metabolites (i.e., non-instrument standards) present in 100% of the samples, which are technical replicates of pooled samples. Values for instrument and process variability meet acceptance criteria of 6% and 13% of median RSD for, respectively, for internal standards and endogenous biochemical.
Compound identification:
Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities was based on comparison to metabolomic library entries of purified standards. The combination of chromatographic properties and mass spectra gave an indication of a match to the specific compound or an isobaric entity. Additional entities could be identified by virtue of their recurrent nature (both chromatographic and mass spectral). These compounds have the potential to be identified by future acquisition of a matching purified standard or by classical structural analysis.
In data pre-processing, contaminants present in FFPE samples (i.e., dimethyl sulfoxide, lauryl sulfate, and melanine) and OCT-embedded samples (i.e., heptaethylene glycol, hexaethylene glycol, octaethylene glycol, pentaethylene glycol, and tetraethylene glycol) were not considered in the analysis. Compounds with more than 90% of missing value were not considered to be reliable and were excluded. Probabilistic Quotient Normalization (PQN) (21) was used to normalize data due to dilution effects in the extraction procedure. For multivariate analysis, compounds with more than 25% of missing values were not used. Otherwise, missing metabolite measurements were imputed using k nearest neighbor (kNN) algorithm (22) with k=5. Data were log-transformed, mean-centered, and scaled to unit variance. The cell line data were centered to the mean of all samples and human samples were centered to the mean of each patient.
Fisher's exact test was used for testing the null hypothesis of independence of rows and columns in a contingency table. Pairwise comparisons were made using the Mann-Whitney test for independent data. Correlation was assessed using the Spearman's rho statistic. The threshold for significance was P<0.05 for all tests. To account for multiple testing, a False Discovery Rate (FDR) of <10% was applied to reduce identification of false positives. FDRs were calculated using the q conversion algorithm (14) in multiple comparison.
Furthermore, Orthogonal Signal Correction (OSC) applied to the Partial Least Square (PLS) model (15), a supervised pattern recognition approach, was used to visualize differences in metabolite composition in samples and as a predictive model in cross-validation analysis using the values of the orthogonal latent variable.
Metabolite Set Enrichment Analysis (MSEA) was carried out using the tool GSEA (Gene Pattern software, Broad Institute, http://genepattern.broadinstitute.org). The metabolite sets were built using the human pathway information available in the Human Metabolome Database (http://www.hmdb.ca). The loadings of OSC-PLS were used for the ranking in the MSEA.
Heatmaps were ordered according to hierarchical clustering (Ward linkage) based on the KODAMA dissimilarity matrix (16) implemented in R package KODAMA. For human FFPE samples of the training set, KODAMA was performed with sample replicates constrained to cluster together. Analyses were carried out using R software (17) with scripts developed in-house.
To compare metabolomic data generated from frozen and FFPE material, prostate cancer isogenic cell lines (i.e., hormone-sensitive LNCaP and castration-resistant LNCaP-Abl) were profiled using untargeted ultrahigh performance liquid chromatography (UPLC)-MS and GC-MS. Using the protocol schematized in
Next, metabolite categorization (i.e., superclass, class, subclass, and metabolic pathway), substituents (an atom or group of atoms taking the place of another atom group or occupying a specific position in a molecule), and chemical/physical properties as annotated in the Human Metabolome Database (HMDB, http://www.hmdb.ca/), Small Molecule Pathway Database (SMPDB, http://smpdb.ca), and Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg) were used to provide a detailed analysis of the metabolites detectable in FFPE samples. As shown in
At the extremes, only 6 peptides of 56 were detected (11%, P=4.56×10−13; FDR=3.65×10−12), whereas 114 lipids of 171 analyzed (67%, P=1.01×104; FDR=4.03×10−4) were preserved in FFPE samples. The majority of fatty acids (93%, P=4.74×10−6; FDR=5.93×10−5), including lysophosphatidylethanolamine (94%, P=4.18×104; FDR=4.73×10−3), glycerolipids (100%, P=2.97×10−3; FDR=2.47×10−2), pyrimidine nucleotides (92%, P=8.24×10−3; FDR=5.15×10−2), and purine nucleotides (85%, P=4.50×10−2; FDR=1.87×10−1), were detectable in FFPE samples, whereas monosaccharides (23%, P=2.11×10−2; FDR=1.05×10−1), phosphatidylcholines (0%, P=1.30×10−3; FDR=1.11×10−2), and lysophosphatidylcholines (46%, P=5.74×10−1; FDR=7.65×10−1) were poorly detectable in FFPE samples. FFPE samples showed a decrease of metabolites with characteristic functional groups, such as secondary carboxylic acid amide (28%, P=7.43×10−12; FDR=6.42×10−10), present in peptides and quaternary ammonium salts (33%, P=1.01×10−3; FDR=2.30×10−2) present in glycerophosphocholines and absent in glycerophosphoethanolamines. No specific depletion of metabolic pathway information was observed. Nonparametric Wilcoxon-Mann-Whitney test was used to evaluate the difference between chemical/physical properties. Lipophilic metabolites showed high detectability in FFPE samples (P=8.09×10−5; FDR=1.29×10−3).
To evaluate data reproducibility in different biological replicates, correlation analyses were performed among the shared metabolites in the five cell culture sets. Pair-wise correlation coefficients were consistently high for both frozen and FFPE samples indicating a minimal variability among replicates. The correlation coefficients, calculated in FFPE cell line samples (
In order to expand metabolomics analyses to retrospective studies confidently, metabolic data from FFPE samples should be consistent with those obtained from frozen material. To test this, the relative concentration of metabolites between frozen and FFPE samples were correlated. A good correlation between the metabolomic data from frozen and FFPE samples was maintained. The correlation coefficients, calculated in cell line samples, ranged between 0.550 and 0.709 (median value of 0.651) (
The reproducibility in the detection of different metabolite classes between FFPE and frozen samples were compared. The correlation coefficients were calculated for each metabolic class (i.e., energy, nucleotides, lipids, amino acids, carbohydrates, cofactors and vitamins) between cell lines replicates. The results, shown in
Metabolic profiling was used to distinguish androgen dependent LNCaP cells from their isogenic, androgen-independent LNCaP-Abl using both frozen and formalin-fixed samples. To perform a comparative analysis between LNCaP and LNCaP-Abl cells, only the shared metabolites found with less than 25% missing values in both frozen and FFPE samples were considered. From among the 189 metabolites retained for analysis, hierarchical clustering based on the KODAMA dissimilarity matrix was applied to show the clear metabolic profiles of LNCaP and LNCaP-Abl cells. This unsupervised method was chosen since it has been previously shown to be very robust even when applied to noisy data (1, 16). Using the 189 shared metabolites between frozen and FFPE samples, the two cell lines were distinguished, with a high degree of accuracy, on the basis of their metabolic profiling in both fixed and frozen states (
Comparing LNCaP and LNCaP-Abl cells, significantly different (Wilcoxon test P<0.05; 10% FDR) 108 metabolites in frozen samples and 65 in FFPE samples were found, with 42 statistically significant in both frozen and FFPE analysis (
OCT-embedded and FFPE tissue blocks were collected from prostatectomy on 12 patients with prostate cancer. Metabolic profiling obtained from matched frozen and FFPE normal and tumor human prostate tissue samples were compared. Samples from 8 patients (training set) were used to define the fingerprint of prostate cancer in FFPE human tissues. Details on tissue and patient features are summarized in Table 12. Samples from the remaining 4 patients were used as an independent set (validation set). A schematic diagram on the sample collection is shown in
A total of 352 and 140 metabolites were detected in frozen and FFPE 2 mm biopsy punch samples, respectively (
Almost all of peptides were not detectable in FFPE samples (3%, P=1.57×10−8; FDR=1.25×10−7). A heterogeneous behavior for the lipid class was observed, with metabolites with good detectability such as fatty acids (68%, P=4.07×10−3; FDR=5.29×10−2) and others like fatty acid esters (19%, P=7.23×10−2; FDR=2.36×10−1) and steroids (0%, P=8.15×10−2; FDR=2.36×10−1) that were poorly detectable. The presence of specific chemical substituents seems to have a clear importance with regards to the ability to detect of metabolites in FFPE samples as suggested by the inferior levels of lysophosphatidylcholines (36%, P=1.00; FDR=1.00) when compared with lysophosphatidylethanolamines (89%, P=4.01×10−3; FDR=3.07×10−2). Significant differences between frozen and FFPE samples, using both cell and human samples, are listed in Table 19, Table 20, Table 21, Table 22, Table 23, and Table 24.
In human FFPE samples, similar to the cell lines, samples correlation coefficients between metabolite concentrations from replicates ranged between 0.920 and 0.994 (median value of 0.979), while from frozen and FFPE samples ranged between 0.471 and 0.698 (median value 0.609), as shown in
Only the 112 metabolites shared in frozen and FFPE samples with less than 25% missing values were considered to delineate the prostate cancer fingerprint. Hierarchical clustering based on KODAMA dissimilarity matrix distinguished normal and tumor prostate tissues (
Tumor and normal frozen tissue samples were able to be separated by hierarchical clustering in both OCT-embedded and FFPE samples. A total of 48 out of 112 metabolites were significantly different between normal and tumor tissue in FFPE samples, whilst 61 out of 112 metabolites were significantly different in frozen samples. Thirty-two metabolites were statistically significant in both frozen and FFPE samples. Results are reported in Table 25 and Table 26, which list metabolite statistical analysis of the differences between normal and tumor prostate tissues in frozen and FFPE samples, respectively. Among the perturbed metabolites found in both OCT-embedded and FFPE samples, 17 were increased in tumor tissue and 13 were down-regulated. Agreement in the direction of metabolite abundance in frozen and FFPE comparisons served as an important indication of the reliability of metabolite detection in FFPE samples.
Next, the coefficient of probabilistic quotient normalization of each sample was correlated with the signal intensity of each metabolite before the normalization step (Table 54). Cytidine 50-diphosphocholine (r=0.905, P=2.77×10−18; FDR=1.55×10−16) was identified as a candidate housekeeping metabolite to adopt in orthogonal metabolic profiling when tissue weight cannot be available for normalization as in the case of FFPE material. An example of the ratio of 2 statistically different metabolites between normal and tumor tissue and cytidine 50-diphosphocholine is reported in
OSC-PLS was used to model the metabolic profile of prostate cancer in frozen and FFPE samples. OSC-PLS is a supervised algorithm that aims to maximize the variance between groups in the latent variable in the output data (i.e., score) and calculates metabolites' loadings that measure importance of the variables in the discrimination between two groups. The OSC-PLS loadings for the discrimination between normal and tumor tissues are shown in
Metabolite Set Enrichment Analysis (MSEA) was performed with the GSEA tool (Gene Pattern software) using the loadings of OSC-PLS to rank the metabolites. The metabolite sets were built using the human pathway information available in the HMDB. The MSEA was used to determine which metabolic pathways were significantly altered between prostate tumors and normal tissue. Alpha-linolenic acid and linoleic acid metabolism was up-regulated in both frozen and FFPE tumor tissues (P=0.012 and FDR=0.064 in frozen tissues, and P=0.050 and FDR=0.166 in FFPE tissues), whereas the up-regulation of protein synthesis was statistically significant only in FFPE samples (P=0.009 and FDR=0.048).
FFPE material was investigated for use in a context of multivariate analysis for diagnostic or prognostic purposes. OSC-PLS was used to model the metabolic profile of prostate cancer in FFPE samples of the training set. The relative OSC-PLS scores plot is shown in the top panel of
From the validation set, biopsy punches were collected from normal and tumor tissues and manual macro-dissection on 20 μm FFPE sections was performed to generate enriched samples for normal or tumor tissue (
It was then investigated whether limited amount of material, such as FFPE sections could be utilized to obtain an accurate metabolic fingerprinting. 20-mm sections from FFPE biopsy punches of the validation set were obtained and manual macrodis section to generate samples enriched for normal or tumor tissue was performed (
Then, whether metabolites could be correlated with stroma and epithelia was tested as an example of mapping metabolites to a particular organelle of tissue or cells. After the metabolic extraction, FPPE tissue sections (on slides) were stained with Hematoxylin and eosin (H&E) showing that the tissue architecture was preserved. A semi-automated algorithm was used to quantify the cell number and the area of the epithelial and stroma compartments (
Non-negative matrix factorization (NMF) was applied to decipher metabolic signatures from stroma and epithelium. 6 metabolic signatures were identified (
Although the procedure to extract metabolites from a tissue section attached to a slide is similar to extraction of the other FFPE samples, it may be complicated by the low quantity of available tissue and the desire to minimize the loss of solution during the extraction. As depicted in the schematics shown in
To extract metabolites from a sample attached to a slide, the slide having the sample is inserted into the cassette depicted in
Potential chemical reasons that might affect selectively specific classes of metabolites during the formalin-fixing and paraffin-embedding process were investigated (
First, metabolites found in the supernatant (n=132) were compared with those found in the extracts from frozen samples (n=437), as described in Table 34, Table 35, Table 36, Table 37, Table 38 and Table 39 to identify those that are soluble in formalin and could, as a result, be lost in the analysis.
The majority of these metabolites were classified as amino acids (and derivatives). Specifically, in the supernatant, 53% of all amino acids present were detected in the frozen samples (P=2.33×10−8; FDR=1.87×10−7). Analyzing their chemical-physical properties, metabolites soluble in formalin are characterized by lower molecular weight (P=2.35×10−24; FDR=2.10×10−23), polarizability (P=6.86×10−23; FDR=3.66×10−22), refractivity (P=2.58×10−20; FDR=2.23×10−19), number of rotatable bond (P=4.80×10−17; FDR=1.54×10−16), and a higher solubility (P=7.81×10−11; FDR=1.56×10−10). Second, metabolites that might be lost in FPPE due to their reaction with formaldehyde when tissues are immersed in a formalin solution were identified. Metabolites interacting with formalin could form covalent bonds with cellular components (insoluble or with high molecular weight) and thus be no longer detectable by MS. Table 40, Table 41, Table 42, Table 43, Table 44, and Table 45 list the metabolites that were not detected in either formalin solution, nor in the extract from FF samples.
Peptides (78%, P=2.29×10−19; FDR=1.84×10−18) and carbohydrates (47%, P=4.09×10−3; FDR=1.09×10−2) probably reacted with formaldehyde. Some metabolites with substituents (an atom or group of atoms taking the place of another atom or group or occupying a specific position in a molecule), such as n-substituted-alphaamino acid (73%, P=1.87×10−15; FDR=2.88×10−13) and carboxamide group (54%, P=1.61×10−14; FDR=1.23×10−12), were severely affected by the fixation procedure, whereas other classes of metabolites, such as fatty acid ester (0%, P=2.33×10−8; FDR=8.90×10−7) and phosphocholine (0%, P=1.13×10−3; FDR=1.84×10−2), remained intact.
These results confirm the analysis reported in Table 46, Table 47, Table 48, Table 49, Table 50, and Table 51 for the comparison between the metabolites found in formalin-fixated and frozen extracts where it was observed that peptides (22%, P=9.70×10−17; FDR=4.69×10−16) and carbohydrates (53%, P=1.32×10−2; FDR=3.51×10−2) were poorly detectable after the fixation procedure. Although amino acid concentration could be severely affected when tissues are immersed in an aqueous solution (i.e., formalin), they were still detectable after the fixation procedure.
Finally, effects of paraffin-embedding on the metabolome were investigated. Metabolites extracted from the samples before and after the paraffin embedding were compared. A global depletion of metabolites in all classes was observed (Table 52, Table 53, Table 54, Table 55, Table 56, and Table 57).
The major depletion was found for membrane lipids, such as glycerophospholipids (60%, P=4.76×10−2; FDR=3.33×10−1). Some metabolites with substituents, such as quaternary ammonium salt (42%, P=2.68×10−6; FDR=6.13×104) and phosphocholine (33%, P=2.35×10−5; FDR=2.69×10−3), were severely affected by the paraffin-embedding procedure.
The relative susceptibility of each class of metabolites to each factor described above (solubility in formalin, the covalent bonding to cellular component, and solubility in ethanol and xylene) is summarized in Table 58.
Taking this into account, a score to rank the reliability of each metabolite on the basis of sensitivity to each factor and to highlight the most stable metabolites during the procedure of formalin fixation and paraffin-embedding was defined. To each metabolite was assigned a score to rank the reliability of its concentration value in extract from FFPE samples. This score ranges from 0 to 3, and it is defined as the sum of the 3 parts. Each part is equal to 1 if the metabolite belongs at the least to one of the selected classes listed in Table 46, otherwise is counted as 0. The basal set of metabolites, that is unchanged despite tissue processing, is represented by the metabolites ranked with a score equal to 0.
While several inventive embodiments have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein, and each of such variations and/or modifications is deemed to be within the scope of the inventive embodiments described herein. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the inventive teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific inventive embodiments described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, inventive embodiments may be practiced otherwise than as specifically described and claimed. Inventive embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the inventive scope of the present disclosure.
All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms.
All references, patents, and patent applications disclosed herein are incorporated by reference with respect to the subject matter for which each is cited, which in some cases may encompass the entirety of the document.
The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.”
The phrase “and/or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.
As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of” or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.
As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.
It should also be understood that, unless clearly indicated to the contrary, in any methods claimed herein that include more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited.
In the claims, as well as in the specification above, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “composed of,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of” and “consisting essentially of” shall be closed or semi-closed transitional phrases, respectively, as set forth in the United States Patent Office Manual of Patent Examining Procedures, Section 2111.03. It should be appreciated that embodiments described in this document using an open-ended transitional phrase (e.g., “comprising”) are also contemplated, in alternative embodiments, as “consisting of” and “consisting essentially of” the feature described by the open-ended transitional phrase. For example, if the disclosure describes “a composition comprising A and B,” the disclosure also contemplates the alternative embodiments “a composition consisting of A and B” and “a composition consisting essentially of A and B.”
This application claims the benefit of U.S. provisional application No. 62/431,627, filed on Dec. 8, 2016, the entire disclosure of which are incorporated by reference herein.
This invention was made with government support under grant number 11498838 awarded by the Department of Defense and grant numbers 2R01CA131945 and P50 CA90381 awarded by National Institutes of Health. The government has certain rights in the invention.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2017/065294 | 12/8/2017 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62431627 | Dec 2016 | US |
